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Abstract Despite advances in drought early warning systems, forecast information is rarely used for triggering and financing early actions, such as cash transfer. Scaling up cash transfer pay-outs, and overcoming the barriers to actions based on forecasts, requires an understanding of costs resulting from False Alarms, and the potential benefits associated with appropriate early interventions. On this study, we evaluate the potential cost-effectiveness of cash transfer responses, comparing the relative costs of ex-ante cash transfers during the maize growing season to ex-post cash transfers after harvesting in Kenya. For that, we developed a forecast model using Fast-and Frugal Trees that unravels early warning relationships between climate variability, vegetation coverage, and maize yields at multiple lead times. Results indicate that our models correctly forecast low maize yield events 85% of the time across the districts studied, some already six months before harvesting. The models' performance improves towards the end of the growing season driven by a decrease of 29% in the probability of False Alarms. Overall, we show that timely cash transfers ex-ante to a disaster can often be more cost-effective than investing in ex-post expenditures. Our findings suggest that early response can yield significant cost savings, and can potentially increase the effectiveness of existing cash transfer systems. Graphical abstract Unlabelled Image Highlights • Drought early warning systems are rarely used for triggering early actions, such as cash transfer. • We evaluate the cost-effectiveness of cash transfers ex-ante (forecast-based) and ex-post. • We developed a forecast model using Machine Learning named Fast-and-Frugal Trees. • We show that our models predict the majority of the low maize yield events in all districts studied. • Our results indicate that cash transfers ex-ante can often be more cost-effective than ex-post. [ABSTRACT FROM AUTHOR]